An optimal programming among renewable energy resources and storage devices for responsive load integration in residential applications using hybrid of grey wolf and shark smell algorithms

This study investigates the effect of responsive load integration on the Microgrids (MGs). Three scenarios have been analyzed to provide a comprehensive understanding of the problem. The results show that increasing the smart load penetration level can influence the network stability, voltage deviation, and load waveform. This study also presents a combination of the grey wolf and shark smell algorithms (GWO + SSO) for optimizing objective functions under several boundary constraints and the proposed method has a high rank of convergence to global minima. A modified 33-bus MG is used which includes wind power, photovoltaic power, and an energy storage system. Moreover, the effect of adding renewable energy resources (RES) and storage devices into the system is evaluated to provide a comparative study. The results show that integration of distributed generation can reduce generation cost and improve network stability by about 20% and 18%, respectively; Note that increasing responsive load may lead to a flatter load profile and provide peak shaving for the system so that the voltage deviation of MG can be improved by about 21%.